{"id":"https://openalex.org/W2959098107","doi":"https://doi.org/10.1109/isdfs.2019.8757538","title":"Predicting Stealthy Watermarks in Files Using Deep Learning","display_name":"Predicting Stealthy Watermarks in Files Using Deep Learning","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2959098107","doi":"https://doi.org/10.1109/isdfs.2019.8757538","mag":"2959098107"},"language":"en","primary_location":{"id":"doi:10.1109/isdfs.2019.8757538","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isdfs.2019.8757538","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 7th International Symposium on Digital Forensics and Security (ISDFS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027503804","display_name":"Maha Farouk S. Sabir","orcid":"https://orcid.org/0000-0002-4233-1647"},"institutions":[{"id":"https://openalex.org/I168959743","display_name":"University of America","ror":"https://ror.org/03s0c9350","country_code":"US","type":"education","lineage":["https://openalex.org/I168959743"]},{"id":"https://openalex.org/I84470341","display_name":"Catholic University of America","ror":"https://ror.org/047yk3s18","country_code":"US","type":"education","lineage":["https://openalex.org/I84470341"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maha F Sabir","raw_affiliation_strings":["Electrical Engineering & Computer Science Department, The Catholic University of America, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering & Computer Science Department, The Catholic University of America, Washington, DC, USA","institution_ids":["https://openalex.org/I168959743","https://openalex.org/I84470341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023335669","display_name":"James H. Jones","orcid":"https://orcid.org/0000-0002-2184-9586"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James H Jones","raw_affiliation_strings":["Electrical & Computer Engineering Department, George Mason University, Fairfax, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering Department, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339066","display_name":"Hang Liu","orcid":"https://orcid.org/0000-0003-4863-8866"},"institutions":[{"id":"https://openalex.org/I168959743","display_name":"University of America","ror":"https://ror.org/03s0c9350","country_code":"US","type":"education","lineage":["https://openalex.org/I168959743"]},{"id":"https://openalex.org/I84470341","display_name":"Catholic University of America","ror":"https://ror.org/047yk3s18","country_code":"US","type":"education","lineage":["https://openalex.org/I84470341"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Liu","raw_affiliation_strings":["Electrical Engineering & Computer Science Department, The Catholic University of America, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering & Computer Science Department, The Catholic University of America, Washington, DC, USA","institution_ids":["https://openalex.org/I168959743","https://openalex.org/I84470341"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074868524","display_name":"Alex V. Mbaziira","orcid":null},"institutions":[{"id":"https://openalex.org/I53276908","display_name":"Marymount University","ror":"https://ror.org/0008kv292","country_code":"US","type":"education","lineage":["https://openalex.org/I53276908"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex V Mbaziira","raw_affiliation_strings":["Data Science & Cyber Security Department, Marymount University, Arlington, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science & Cyber Security Department, Marymount University, Arlington, VA, USA","institution_ids":["https://openalex.org/I53276908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.06128458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"14","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12034","display_name":"Digital and Cyber Forensics","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7649358510971069},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.7064770460128784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6950150728225708},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5150008201599121},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4615516662597656},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.43570128083229065},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4285215735435486},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4122447371482849},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36528754234313965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7649358510971069},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.7064770460128784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6950150728225708},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5150008201599121},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4615516662597656},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.43570128083229065},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4285215735435486},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4122447371482849},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36528754234313965},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isdfs.2019.8757538","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isdfs.2019.8757538","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 7th International Symposium on Digital Forensics and Security (ISDFS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1503100773","https://openalex.org/W2028642251","https://openalex.org/W2076342816","https://openalex.org/W2133990480","https://openalex.org/W2412509443","https://openalex.org/W2444185264","https://openalex.org/W2579318729","https://openalex.org/W2771036112","https://openalex.org/W2805747897","https://openalex.org/W2808467956","https://openalex.org/W2937081530","https://openalex.org/W3008653841","https://openalex.org/W3100675441","https://openalex.org/W6630263520","https://openalex.org/W6746523225","https://openalex.org/W6774882560"],"related_works":["https://openalex.org/W2783354812","https://openalex.org/W4384112194","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4308259661","https://openalex.org/W4390813131","https://openalex.org/W2349383066","https://openalex.org/W4328132048","https://openalex.org/W1969901537","https://openalex.org/W2119768059"],"abstract_inverted_index":{"Digital":[0],"evidence":[1],"is":[2,17,55],"a":[3,50],"fundamental":[4],"element":[5],"in":[6,43,83,98,140,211,225,237,255,260],"cyber-forensics":[7],"and":[8,26,60,91,110,126],"judicial":[9],"processes.":[10],"However,":[11,181],"the":[12,22,86,106,114,121,129,239],"work":[13],"of":[14,46,66,108,123,177,194,257],"forensic":[15],"examiners":[16],"becoming":[18],"more":[19],"challenging":[20],"as":[21,96],"volume":[23],"digital":[24],"content":[25],"files":[27,84,141,213,262],"increases.":[28],"In":[29],"this":[30],"paper,":[31],"we":[32,202],"use":[33,49],"machine":[34],"learning":[35,38,88,251],"especially":[36],"deep":[37,87,250],"to":[39,63,79,148,159,170,179,187,196,233,244],"detect":[40],"stealthy":[41,81,138,209,223,258],"watermarks":[42,82,139,210,224,259],"various":[44],"types":[45],"files.":[47],"We":[48,73,100],"black":[51],"box":[52],"approach":[53],"which":[54,207],"different":[56],"from":[57,146,157,168,231,242],"current":[58],"steganographic":[59],"cryptographic":[61],"methods":[62],"find":[64],"patterns":[65],"candidate":[67],"file":[68],"locations":[69],"for":[70,199],"hidden":[71,111],"data":[72],"studied":[74],"Deep":[75],"Neural":[76],"Networks":[77],"(DNN)":[78],"predict":[80],"using":[85,263],"implementation":[89],"(DL4J)":[90],"Multilayer":[92],"Perceptron":[93],"(MLP)":[94],"algorithms":[95],"implemented":[97],"Weka.":[99],"evaluated":[101,118],"MLP":[102,130],"models":[103,116,136,152,163],"by":[104,119],"altering":[105],"number":[107,122],"neurons":[109],"layers":[112,125],"while":[113,172,189,236],"DL4J":[115,200,220,264],"were":[117],"varying":[120],"dense":[124],"nodes.":[127],"For":[128],"models,":[131,201,206],"DOCX":[132,212],"&":[133],"PPTX":[134,226,261],"singleton":[135,151],"predicted":[137,208,222],"with":[142,214,227,249],"predictive":[143,154,165,175,184,192,215,228,240],"accuracies":[144,155,166,176,185,193,216,229,241],"ranging":[145,156,167,230],"47.5%":[147,178,186],"100%;":[149],"JPEG":[150],"registered":[153],"35%":[158],"65%.":[160],"Comparatively,":[161],"HYBRID3":[162,205,219],"had":[164,174,183,191],"42.5%":[169],"95%":[171],"HYBRID_OOXML":[173],"100%.":[180,217],"JPEG_DOCX":[182],"97.5%":[188],"JPEG_PPTX":[190],"40%":[195],"85%.":[197],"Furthermore":[198],"only":[203],"generated":[204],"The":[218,246],"model":[221],"55%":[232],"82":[234],"%":[235],"JPEG,":[238],"50%":[243],"52.5%.":[245],"major":[247],"finding":[248],"also":[252],"revealed":[253],"improvements":[254],"prediction":[256],"models.":[265]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}